Overview

Dataset statistics

Number of variables8
Number of observations2421
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory153.7 KiB
Average record size in memory65.0 B

Variable types

DateTime1
TimeSeries6
Boolean1

Timeseries statistics

Number of series6
Time series length2421
Starting point2010-01-04 00:00:00
Ending point2019-08-19 00:00:00
Period1 day, 10 hours and 50 minutes
2026-02-01T22:32:22.315464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:22.479733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

repaired? has constant value "False"Constant
adj close is highly overall correlated with close and 4 other fieldsHigh correlation
close is highly overall correlated with adj close and 4 other fieldsHigh correlation
high is highly overall correlated with adj close and 4 other fieldsHigh correlation
low is highly overall correlated with adj close and 4 other fieldsHigh correlation
open is highly overall correlated with adj close and 4 other fieldsHigh correlation
volume is highly overall correlated with adj close and 4 other fieldsHigh correlation
adj close is non stationaryNon stationary
close is non stationaryNon stationary
high is non stationaryNon stationary
low is non stationaryNon stationary
open is non stationaryNon stationary
volume is non stationaryNon stationary
adj close is seasonalSeasonal
close is seasonalSeasonal
high is seasonalSeasonal
low is seasonalSeasonal
open is seasonalSeasonal
volume is seasonalSeasonal
Date has unique valuesUnique

Reproduction

Analysis started2026-02-02 04:32:19.090243
Analysis finished2026-02-02 04:32:22.228589
Duration3.14 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Date
Date

Unique 

Distinct2421
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
Minimum2010-01-04 00:00:00
Maximum2019-08-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-01T22:32:22.697679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:22.778488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

adj close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct89
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.116894
Minimum26
Maximum114
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:22.878029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile42
Q152
median74
Q394
95-th percentile105
Maximum114
Range88
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.083743
Coefficient of variation (CV)0.30203338
Kurtosis-1.384764
Mean73.116894
Median Absolute Deviation (MAD)21
Skewness-0.034750164
Sum177016
Variance487.6917
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6003828664
2026-02-01T22:32:22.966465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:23.204031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:23.891916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9767
 
2.8%
4665
 
2.7%
4964
 
2.6%
9362
 
2.6%
4862
 
2.6%
5359
 
2.4%
5059
 
2.4%
4556
 
2.3%
9452
 
2.1%
5252
 
2.1%
Other values (79)1823
75.3%
ValueCountFrequency (%)
261
 
< 0.1%
272
 
0.1%
282
 
0.1%
293
0.1%
307
0.3%
317
0.3%
327
0.3%
335
0.2%
344
0.2%
355
0.2%
ValueCountFrequency (%)
1142
 
0.1%
1133
 
0.1%
1123
 
0.1%
1113
 
0.1%
1106
 
0.2%
1098
 
0.3%
10815
0.6%
10732
1.3%
10626
1.1%
10529
1.2%
2026-02-01T22:32:23.042950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct89
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.116894
Minimum26
Maximum114
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:24.331810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile42
Q152
median74
Q394
95-th percentile105
Maximum114
Range88
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.083743
Coefficient of variation (CV)0.30203338
Kurtosis-1.384764
Mean73.116894
Median Absolute Deviation (MAD)21
Skewness-0.034750164
Sum177016
Variance487.6917
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6003828664
2026-02-01T22:32:24.406350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:24.577482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:25.296373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9767
 
2.8%
4665
 
2.7%
4964
 
2.6%
9362
 
2.6%
4862
 
2.6%
5359
 
2.4%
5059
 
2.4%
4556
 
2.3%
9452
 
2.1%
5252
 
2.1%
Other values (79)1823
75.3%
ValueCountFrequency (%)
261
 
< 0.1%
272
 
0.1%
282
 
0.1%
293
0.1%
307
0.3%
317
0.3%
327
0.3%
335
0.2%
344
0.2%
355
0.2%
ValueCountFrequency (%)
1142
 
0.1%
1133
 
0.1%
1123
 
0.1%
1113
 
0.1%
1106
 
0.2%
1098
 
0.3%
10815
0.6%
10732
1.3%
10626
1.1%
10529
1.2%
2026-02-01T22:32:24.460591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct88
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.052045
Minimum27
Maximum115
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:25.767651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile43
Q153
median75
Q395
95-th percentile106
Maximum115
Range88
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.139989
Coefficient of variation (CV)0.29897877
Kurtosis-1.3968278
Mean74.052045
Median Absolute Deviation (MAD)21
Skewness-0.033706364
Sum179280
Variance490.17911
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6186229194
2026-02-01T22:32:25.858397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:26.077320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:26.965920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9465
 
2.7%
9864
 
2.6%
4764
 
2.6%
5363
 
2.6%
4961
 
2.5%
5460
 
2.5%
4860
 
2.5%
5059
 
2.4%
4656
 
2.3%
10055
 
2.3%
Other values (78)1814
74.9%
ValueCountFrequency (%)
271
 
< 0.1%
292
 
0.1%
303
 
0.1%
315
0.2%
3211
0.5%
335
0.2%
347
0.3%
356
0.2%
364
 
0.2%
379
0.4%
ValueCountFrequency (%)
1151
 
< 0.1%
1142
 
0.1%
1136
 
0.2%
1123
 
0.1%
1113
 
0.1%
1107
 
0.3%
10921
0.9%
10824
1.0%
10733
1.4%
10623
1.0%
2026-02-01T22:32:25.938269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

low
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct87
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.118959
Minimum26
Maximum112
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:27.418513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile41
Q151
median73
Q393
95-th percentile104
Maximum112
Range86
Interquartile range (IQR)42

Descriptive statistics

Standard deviation21.953423
Coefficient of variation (CV)0.30440571
Kurtosis-1.3783931
Mean72.118959
Median Absolute Deviation (MAD)21
Skewness-0.031287718
Sum174600
Variance481.95279
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.5870544091
2026-02-01T22:32:27.507187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:27.695738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:28.391192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4863
 
2.6%
9761
 
2.5%
9361
 
2.5%
4560
 
2.5%
9557
 
2.4%
4957
 
2.4%
4757
 
2.4%
4456
 
2.3%
5155
 
2.3%
4655
 
2.3%
Other values (77)1839
76.0%
ValueCountFrequency (%)
262
 
0.1%
272
 
0.1%
283
 
0.1%
297
0.3%
309
0.4%
316
0.2%
324
0.2%
334
0.2%
346
0.2%
356
0.2%
ValueCountFrequency (%)
1122
 
0.1%
1115
 
0.2%
1102
 
0.1%
1093
 
0.1%
1089
 
0.4%
10717
0.7%
10629
1.2%
10529
1.2%
10440
1.7%
10330
1.2%
2026-02-01T22:32:27.575457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct88
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.141677
Minimum27
Maximum114
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:28.831223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile42
Q152
median74
Q394
95-th percentile105
Maximum114
Range87
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.068324
Coefficient of variation (CV)0.30172023
Kurtosis-1.386194
Mean73.141677
Median Absolute Deviation (MAD)21
Skewness-0.033200776
Sum177076
Variance487.01091
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6128440983
2026-02-01T22:32:28.898333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:29.071525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:29.761204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4968
 
2.8%
9765
 
2.7%
9364
 
2.6%
4664
 
2.6%
4562
 
2.6%
5160
 
2.5%
5359
 
2.4%
4858
 
2.4%
5254
 
2.2%
9452
 
2.1%
Other values (78)1815
75.0%
ValueCountFrequency (%)
272
 
0.1%
283
 
0.1%
293
 
0.1%
305
0.2%
3110
0.4%
324
 
0.2%
336
0.2%
345
0.2%
355
0.2%
368
0.3%
ValueCountFrequency (%)
1141
 
< 0.1%
1134
 
0.2%
1123
 
0.1%
1112
 
0.1%
1104
 
0.2%
10910
 
0.4%
10819
0.8%
10731
1.3%
10629
1.2%
10527
1.1%
2026-02-01T22:32:28.952873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

repaired?
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
False
2421 
ValueCountFrequency (%)
False2421
100.0%
2026-02-01T22:32:30.166150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

volume
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2414
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean415421.09
Minimum48516
Maximum1311000
Zeros0
Zeros (%)0.0%
Memory size37.8 KiB
2026-02-01T22:32:30.232713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48516
5-th percentile150682
Q1253202
median350221
Q3558344
95-th percentile806219
Maximum1311000
Range1262484
Interquartile range (IQR)305142

Descriptive statistics

Standard deviation212266.63
Coefficient of variation (CV)0.5109674
Kurtosis0.097068676
Mean415421.09
Median Absolute Deviation (MAD)128536
Skewness0.82924818
Sum1.0057345 × 109
Variance4.5057124 × 1010
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2029823001
2026-02-01T22:32:30.306001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T22:32:30.479156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps502
min3 days
max5 days
mean3 days, 3 hours and 12 minutes
std8 hours, 18 minutes and 34.69 seconds
2026-02-01T22:32:31.207186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2263432
 
0.1%
8956432
 
0.1%
5988362
 
0.1%
3179972
 
0.1%
2975222
 
0.1%
1324272
 
0.1%
2500382
 
0.1%
4884651
 
< 0.1%
4063861
 
< 0.1%
4769881
 
< 0.1%
Other values (2404)2404
99.3%
ValueCountFrequency (%)
485161
< 0.1%
518771
< 0.1%
656661
< 0.1%
837021
< 0.1%
846271
< 0.1%
908241
< 0.1%
913981
< 0.1%
927801
< 0.1%
931301
< 0.1%
952701
< 0.1%
ValueCountFrequency (%)
13110001
< 0.1%
12535661
< 0.1%
11823271
< 0.1%
11735811
< 0.1%
11473891
< 0.1%
11362321
< 0.1%
11354401
< 0.1%
11249591
< 0.1%
11080521
< 0.1%
10956431
< 0.1%
2026-02-01T22:32:30.360819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2026-02-01T22:32:21.742401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:19.926562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.287364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.717005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.030255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.357108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.804066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:19.981814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.342757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.770350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.081111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.409377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.862660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.032566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.435513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.821002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.133367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.458507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.920439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.096125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.554631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.871205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.181720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.508494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.976410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.158485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.607035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.919884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.241469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.559252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:22.037186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.210122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.659371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:20.969867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.300707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T22:32:21.632984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-01T22:32:31.617786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adj closeclosehighlowopenvolume
adj close1.0001.0000.9990.9990.997-0.601
close1.0001.0000.9990.9990.997-0.601
high0.9990.9991.0000.9980.999-0.596
low0.9990.9990.9981.0000.998-0.606
open0.9970.9970.9990.9981.000-0.599
volume-0.601-0.601-0.596-0.606-0.5991.000

Missing values

2026-02-01T22:32:22.126363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-01T22:32:22.184144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Dateadj closeclosehighlowopenrepaired?volume
2010-01-042010-01-048282828080False263542
2010-01-052010-01-058282828182False258887
2010-01-062010-01-068383848181False370059
2010-01-072010-01-078383838283False246632
2010-01-082010-01-088383838283False310377
2010-01-112010-01-118383848283False296304
2010-01-122010-01-128181828082False333866
2010-01-132010-01-138080817880False401627
2010-01-142010-01-147979807980False275404
2010-01-152010-01-157878797879False200555
Dateadj closeclosehighlowopenrepaired?volume
2019-08-062019-08-065454555355False682794
2019-08-072019-08-075151545153False1063214
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